Detection of long-term trends in monthly hydro-climatic series of Colombia through Empirical Mode Decomposition
We test for the existence of long-term trends in 25- to 50-year long series of monthly rainfall, average river discharges, and minimum air temperatures in Colombia. The Empirical Mode Decomposition method is used as a mathematical filter to decompose a given time series into a finite number of intrinsic mode functions, assuming the coexistence of different frequency oscillatory modes in the series, and that the residual captures the likely existing long-term trends. The Mann-Kendall test for autocorrelated data is used to assess the statistical significance of the identified trends, and the Sen test is used to quantify their magnitudes. Results show that 62 % of river discharge series exhibit significant decreasing trends between 0.01-1.92 m 3 s −1 per year, which are highly consistent downstream albeit with different ratios between the trend magnitudes and mean discharges. Most minimum temperature series (87 %) exhibit increasing trends (0.01-0.08 °Cyr −1 ). Results for precipitation series are inconclusive owing to the mixing between increasing trends (41 %, between 0.1-7.0 mm yr −1 ) and decreasing trends (44 %, between 0.1-7.4 mm yr −1 ), with no clear-cut geographical pattern, except for the increasing trend identified along the Pacific region, consistent with the increasing trend identified in the strength of the Chocó low-level wind jet off the Pacific coast of Colombia, an important moisture source of continental precipitation. Our results contribute to discerning between signals of climate change and climate variability in tropical South America. Copyright Springer Science+Business Media Dordrecht 2014
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Volume (Year): 123 (2014)
Issue (Month): 2 (March)
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- Moghtaderi, Azadeh & Flandrin, Patrick & Borgnat, Pierre, 2013. "Trend filtering via empirical mode decompositions," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 114-126.
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